Common Analysis Trees

The Spin PWG maintains a set of trees connecting datasets from the various inclusive measurements in a way that allows for easy particle correlation studies. This page describes how to access the data in those trees.

The last option uses xrootd to access read-only files stored on an MIT server from any computer with ROOT installed. If you have an Intel Mac note that ROOT versions 5.13.06 - 5.14.00 have a bug (patched in 5.14.00/b) that prevents you from opening xrootd files.

Interactive Mode

The basic trees are readable in a simple interactive ROOT session. Each particle type is stored in a separate tree, so you need to use TTree::AddFriend to connect things together before you draw. For example:

root [1] TFile::Open(&quot;root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root&quot;); root [2] .ls TXNetFile** root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root TXNetFile* root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root KEY: TProcessID ProcessID0;1 00013b6e-72c3-1640-a0e8-e5243780beef KEY: TTree spinTree;1 Spin PWG common analysis tree KEY: TTree ConeJets;1 this can be a friend KEY: TTree ConeJetsEMC;1 this can be a friend KEY: TTree chargedPions;1 this can be a friend KEY: TTree bemcPions;1 this can be a friend root [3] spinTree-&gt;AddFriend(&quot;ConeJets&quot;); root [4] spinTree-&gt;AddFriend(&quot;chargedPions&quot;); root [5] spinTree-&gt;Draw(&quot;chargedPions.fE / ConeJets.fE&quot;,&quot;chargedPions.fE&gt;0&quot;) If you have the class definitions loaded you can also access member functions directly in the interpreter:

Batch Mode

The StSpinTreeReader class takes care of all the details of setting branch addresses for the various particles behind the scenes. It also allows you to supply a runlist and a set of triggers you're interested in, and it will only read in the events that you care about. The code lives in

StRoot/StSpinPool/StSpinTree

and in the macros directory is an example showing how to configure it. Let's look at the macro step-by-step:

//create a new reader StSpinTreeReader *reader = new StSpinTreeReader(); //add some files to analyze, one at a time or in a text file reader-&gt;selectDataset(&quot;$STAR/StRoot/StSpinPool/StSpinTree/datasets/run6_rcf.dataset&quot;); //reader-&gt;selectFile(&quot;./spinAnalyses_6119039.tree.root&quot;); Ok, so we created a new reader and told it we'd be using the files from Run 6 stored on RCF. You can also give it specfic filenames if you'd prefer, but there's really no reason to do so.

//configure the branches you're interested in (default = true) reader-&gt;connectJets = true; reader-&gt;connectNeutralJets = false; reader-&gt;connectChargedPions = true; reader-&gt;connectBemcPions = true; reader-&gt;connectEemcPions = false; reader-&gt;connectBemcElectrons = false; //optionally filter events by run and trigger //reader-&gt;selectRunList(&quot;$STAR/StRoot/StSpinPool/StSpinTree/filters/run6_jets.runlist&quot;); reader-&gt;selectRun(7143025); //select events that passed hardware OR software trigger for any trigger in list reader-&gt;selectTrigger(137221); reader-&gt;selectTrigger(137222); reader-&gt;selectTrigger(137611); reader-&gt;selectTrigger(137622); reader-&gt;selectTrigger(5); //we can change the OR to AND by doing reader-&gt;requireDidFire = true; reader-&gt;requireShouldFire = true; In this block we configured the reader to pick up the jets, chargedPions and BEMC pi0s from the files. We also told it that we only wanted to analyze run 7132001, and that we only cared about events triggered by BJP1, L2jet, or L2gamma in the second longitudinal running period. Finally, we required that one of those trigIds passed both the hardware and the software triggers.

After that, the reader behaves pretty much like a regular TChain. The first time you call GetEntries() will be very slow (few minutes for the full dataset) as that's when the reader chains together the files and applies the TEventList with your trigger selection. Each of the particles is stored in a TClonesArray, and the StJetSkimEvent is accessible via reader->event().

Known Issues

These are harmless (somehow related to custom Streamers in the StarClassLibrary) but I haven't yet figured out how to shut them up.

42 runs need to be reprocessed for chargedPions in Run 5. Will do once Andrew gives the OK at PDSF.

40 runs need to be reprocessed for Run 6 because of MuDst problems. Murad has also mentioned some problems with missing statistics in the skimEvents and jet trees that we'll revisit at a later date.

Future Plans

Including EEMC pi0s and StGammaCandidates remains on my TO-DO list. I've also added into StJet a vector of trigger IDs fired by that jet. Of course we also need to get L2 trigger emulation into the skimEvent. As always, if you have questions or problems please feel free to contact me.

Cuts Summary

Here's a list of the cuts applied to the data in the common spin trees.

Introduction at Spin PWG meeting - 5/10/07

I've been working on a project to make the datasets from the various longitudinal spin analyses underway at STAR available in a common set of trees. These trees would improve our ability to do the kind of correlation studies that are becoming increasingly important as we move beyond inclusive analyses in the coming years.

In our current workflow, each identified particle analysis has one or more experts responsible for deciding just which reconstruction parameters and cuts are used to determine a good final dataset. I don't envision changing that. Rather, I am taking the trees produced by those analyzers as inputs, picking off the essential information, and feeding it into a single common tree for each run. I am also providing a reader class in StSpinPool that takes care of connecting the various branches and does event selection given a run list and/or trigger list.

Features

Readable without the STAR framework

Condenses data from several analyses down to the most essential ~10 GB (Run 6)

Current Status

I'm waiting on the skimEvent reproduction to finish before releasing. I've got the codes to combine jets, charged pions, and BEMC pions, and I'm working with Jason and Priscilla on EEMC pions and BEMC electrons.

xy distribution of SMD hits

The separation between photons and pions was achieved by using Les cut in the above figures where photons reside above the curve and pions below. The data set used is the st_jpsi express stream.

Single peak characteristics

Fit function

The transverse profile of an electromagnetic shower in the SMD can be parametrized by the equation below in each SMD plane:

f(x) is the energy in MeV as a function of SMD strip x. The algorithm performs a simultaneous fit in both the U and V plane. The maximal residual (data - fit) is then calculated. A single photon in the SMD should be well descibed by the equation above and therefore will have a smaller maximal residual. A neutral pion, which decays into two photons, should exhibit a larger maximal residual. Typically, the response would be a double peak, possibly a larger peak and a smaller peak corresponding to a softer photon.

Single event SMD response

This directory contains images of single event SMD responses in both U and V plane. The file name convention is SMD_RUN_EVENT.png. The fit function for a single peak is the one described in the section above with 5 parameters:

p0 = yield (P0), area under the peak in MeV

p1 = mean (μ), center of peak in strips

p2 = sigma of the first Gaussian (w1)

p3 = fraction of the amplitude of the second Gaussian with respect to the first one (B), fixed to 0.2

p4 = ratio of the width of the second Gaussian to the width of the first one (w2/w1), fixed to 3.5

EEMC Fast Simulator

EEMC Slow Simulator

2006.09.15 Fit Parameters

Fit Parameters

Fit Function

The plots that follow are sums of individual SMD responses in each plane centered around a common mean (here 0), over a +/-40 strips range. The convention for the parameters in the fits below is:

p0=E -- area under the curve which represents energy in MeV

p1=μ -- mean

p2=σcore -- width of the narrow Gaussian

p3=γ -- relative contribution of the wide Gaussian to the area/height

p4=σtail -- width of the wide Gaussian

Simulation

The simulation is from single photons thrown at the EEMC with the following pT distribution:

The highest tower above 4 GeV in total energy is selected and the corresponding SMD sector fitted for peaks in both planes, where the area of the peaks in the U plane is constrained to be identical to that of the peak in the V plane. The peak is shifted to be centered at 0 where peaks from other events are then summed. The summed SMD response in each plane is displayed below:

Ditto in log scale.

Ditto by sector.

Fit Widths

Sector #

SMD-u σcore

SMD-u σtail

SMD-v σcore

SMD-v σtail

Sector 1

0.869033 ± 0.0142868

3.42031 ± 0.10226

0.84379 ± 0.0185107

3.03287 ± 0.0775009

Sector 2

0.814959 ± 0.0169271

2.99941 ± 0.0730426

0.889892 ± 0.0163065

3.35288 ± 0.0911979

Sector 3

0.84862 ± 0.0148706

3.07648 ± 0.0909689

0.914377 ± 0.014706

3.72821 ± 0.0966915

Sector 4

0.924398 ± 0.0144207

3.74458 ± 0.10611

0.888146 ± 0.0180771

3.06618 ± 0.0647075

Sector 5

0.934218 ± 0.0163887

3.45149 ± 0.0944309

0.911209 ± 0.0175273

3.28633 ± 0.0890581

Sector 6

0.797976 ± 0.0148133

3.20464 ± 0.0986085

0.822437 ± 0.018835

3.30595 ± 0.118813

Sector 7

0.836936 ± 0.0150085

3.28589 ± 0.0853598

0.873338 ± 0.0173883

3.16654 ± 0.0838938

Sector 8

0.828403 ± 0.0167005

3.05517 ± 0.075584

0.891045 ± 0.0152102

3.34806 ± 0.0836394

Sector 9

0.832881 ± 0.0127855

3.3214 ± 0.0762928

0.8436 ± 0.0175466

3.0183 ± 0.079444

Sector 10

0.804059 ± 0.0160906

3.0943 ± 0.0897946

0.874845 ± 0.015788

3.18113 ± 0.0748357

Sector 11

0.930286 ± 0.0187086

3.40024 ± 0.0951671

0.854395 ± 0.0167265

3.21076 ± 0.0812402

Sector 12

3.33227 ± 0.111911

0.848668 ± 0.0142344

0.895174 ± 0.0160939

3.48527 ± 0.12061

Data from 2005 pp200 EEMC HT 1 and 2 triggers

In this sample, high tower triggers, eemc-ht1-mb (96251) and eemc-ht2-mb (96261), from the 2005 p+p at √s=200 GeV ppProduction are selected. The highest tower above 4 GeV is chosen and the corresponding SMD sector is searched for peaks in both planes. Peaks from several events are summed together taking care of shifting them around to have a common mean.

Ditto in log scale.

Data from 2005 pp200 electrons

Here, I try to pick a representative sample of electrons from the 2005 pp200 dataset. The cuts used to pick out electrons are:

Epreshower1 > 5 MeV

Epreshower2 > 5 MeV

0.75 < p/Etower < 1.25

3 < dE/dx < 4 keV/cm

The selection for electrons is illustrated in the dE/dx plot below, where the pions should be on the left and the electrons on the right.

Pibero DjawothoLast modified Tue Aug 15 10:41:19 EDT 2006

2007.02.05 Reconstructed/Monte Carlo Photon Energy

Reconstructed/Monte Carlo Photon Energy

This study is motivated by Weihong's photon energy loss study where an eta-dependence of reconstructed photon energy to generated photon energy in EEMC simulation was observed.

In this study, the eta-dependence is investigated by running the EEMC slow simulator with the new readjusted weights for the preshower and postshower layers of the EEMC. Details on this are here.

Fit to a constant

Fit to a line

Fit to a quadratic

Comparison between Weihong's and Pibero's results

The parameters from the fits are used to plot the fit functions for comparison between Weihong's and Pibero's results.

Constant

Linear

Quadratic

Conclusion

While the adjusted weights for the different EEMC layers contribute to bringing the ratio of reconstructed energy to generated energy closer to unity, they do not remove the eta-dependence.

Summary of Reconstructed/Monte Carlo Photon Energy

Description

For each photon energy, the ratio E_reco/E_MC vs. eta was plotted and fitted to the function p0+p1*(1-eta), where E_reco is the reconstructed photon energy integrated over the

entire

EEMC. The range of the fit was fixed from 1.15 to 1.95 to avoid EEMC edge effects. The advantage of parametrizing the eta-dependence of the ratio in this way is that p0 is immediately interpretable as the ratio in the middle of the EEMC. The parameters p0 and p1 vs. photon energy were subsequently plotted for the EEMC fast and slow simulator.

EEMC Fast/Slow Simulator Results

Conclusions

The parameter p0, i.e. the ratio E_reco/E_MC in the mid-region of the EEMC, increases monotonically from 0.74 at 5 GeV to 0.82 at 160 GeV, and the parameter p1, i.e. the slope of the eta-dependence, also increases monotonically from -0.035 at 5 GeV to 0.038 at 160 GeV. There appears to be a magic energy around 10 GeV where the response of the EEMC is nearly flat across its entire pseudorapidity range. The anomalous slope p1 at 160 GeV for the EEMC slow simulator is an EEMC hardware saturation effect. The EEMC uses 12-bit ADC's for reading out tower transverse energies and is set for a 60 GeV range. Any particle which deposits more than 60 GeV in E_T will be registered as depositing only 60 GeV as the ADC will return the maximum value of 4095. This translates into a limit on the eta range of the EEMC for a particular energy. Let's say that energy is 160 GeV and the EEMC tops at 60 GeV in E_T, then the minimum eta is acosh(160/60)=1.6. This limitation is noticeable in a plot of E_reco/E_MC vs. eta. This anomaly is not observed in the result of the EEMC fast simulator because the saturation behavior was not implemented at the time of the simulation (it has since been corrected). The energy-dependence of the parameter p0 is fitted to p0(E)=a+b*log(E) and the parameter p1 to p1(E)=a+b/log(E). The results are summarized below:

2007.05.30 Efficiency of reconstructing photons in EEMC

Efficiency of reconstructing photons in EEMC

Monte Carlo sample

SMD gamma/pi0 discrimination algorithm

from the IUCF STAR Web site gives a brief overview of the SMD gamma/pi0 discrimination algorithm using the method of maximal sided fit residual (data - fit). This technique comes to STAR EEMC from the Tevatron via Les Bland via Jason Webb. The specific fit function used in this analysis is:

x is the strip id in the SMD-u or SMD-v plane. The widths of the narrow and wide Gaussians are determined from empirical fits of shower shape response in the EEMC from simulation.

Optimizing cuts for gamma/pi0 separation

In the rest of this analysis, only those photons which have reconstructed pt > 5 GeV are kept. There is no requirement that the photon doesn't convert. The dividing curve between photons and pions is:

f(x)=4*x+1e-7*x**5

The y-axis is integrated yield over the SMD-u and SMD-v plane, and the x-axis is the sum of the maximal sided residual of the SMD-u and SMD-v plane.

Following exchanges with Scott Wissink, the idea is to move from a quintic to a quadratic to reduce the number of parameters. In addition, the perpendicular distance between the curve and a point in the plane is used to estimate the likelihood of a particle being a photon or pion. Distances above the curve are positive and those below are negative. The more positive the distance, the more likely the particle is a photon. The more negative the distance, the more likely the particle is a pion.

Hi Pibero,
With your new "linear plus quintic" curve (!) ... how did you choose the
coefficients for each term? Or even the form of the curve? I'm not
being picky, but how to optimize such curves will be an important issue
as we (hopefully soon) move on to quantitative comparisons of efficiency
vs purity.
As a teaser, please see attached - small loss of efficiency, larger gain
in purity.
Scott

Hi Pibero,
I just worked out the distance of closest approach to a curve of the form
y(x) = a + bx^2
and it involves solving a cubic equation - so maybe not so trivial after
all. But if you want to pursue this (not sure it is your highest
priority right now!), the cubic could be solved numerically and "alpha"
could be easily calculated.
More fun and games.
Scott

Hi Pibero,
I played around with the equations a bit more, and I worked out an
analytic solution. But a numerical solution may still be better, since
it allows more flexibility in the algebraic form of the 'boundary' line
between photons and pions.
Here's the basic idea: suppose the curved line that cuts between
photons and pions can be expressed as y = f(x). If we are now given a
point (x0,y0) in the plane, our goal is to find the shortest distance to
this line. We can call this distance d (I think on your blackboard we
called it alpha).
To find the shortest distance, we need a straight line that passes
through (x0,y0) and is also perpendicular to the curve f(x). Let's
define the point where this straight line intersects the curve as
(x1,y1). This means (comparing slopes)
(y1 - y0) / (x1 - x0) = -1 / f'(x1)
where f'(x1) is the derivative of f(x) evaluated at the point (x1,y1).
Rearranging this, and using y1 = f(x1), yields the general result
f(x1) f'(x1) - y0 f'(x1) + x1 - x0 = 0
So, given f(x) and the point (x0,y0), the above is an equation in only
x1. Solve for x1, use y1 = f(x1), and then the distance d of interest
is given by
d = sqrt[ (x1 - x0)^2 + (y1 - y0)^2 ]
Example: suppose we got a reasonable separation of photons and pions
using a curve of the form
y = f(x) = a + bx^2
Using this in the above general equation yields the cubic equation
(2b^2) x1^3 + (2ab + 1 - 2by0) x1 - x0 = 0
Dividing through by 2b^2, we have an equation of the form
x^3 + px + q = 0
This can actually be solved analytically - but as I mentioned, a
numerical approach gives us more flexibility to try other forms for the
curve, so this may be the way to go. I think (haven't proved
rigorously) that for positive values of the constants a, b, x0, and y0,
the cubic will yield three real solutions for x1, but only one will have
x1 > 0, which is the solution of interest.
Anyway, it has been an interesting intellectual exercise!
Scott

I made use of the ROOT function TMath::RootsCubic to solve the cubic equation numerically for computing distances of each point to the curve. With the new quadratic curve f(x)=100+0.1*x^2 the efficiency is 63% and the rejection is 82%.

Efficiency and Rejection

The plot on the left below shows the efficiency of identifying photons over the pt range of 10-30 GeV and the one on the right shows the rejection rate of single neutral pions. Both average about 75% over the pt range of interest.

Rejection vs. efficiency at different energies

Rejection vs. efficiency with preshower cut

Below on the left is a plot of the ratio of the sum of preshower 1 and 2 to tower energy for both photons (red) and pions (blue). On the right is the rejection of pions vs. efficiency of photons as I cut on the ratio of preshower to tower. It is clear from these plots that the preshower layer is not a good gamma/pi0 discriminator, although can be used to add marginal improvement to the separation preovided by the shower max.

2007.06.12 gamma/pi0 separation in EEMC at pT 5-10 GeV

gamma/pi0 separation in EEMC at pT 5-10 GeV

2007.06.28 Photons in Pythia

Pythia Simulations

Pythia Simulations

All partonic pT

The plots below show the distribution of clusters in the endcap calorimeter for different partonic pT ranges. 2000 events were generated for each pT range. A cluster is made up of a central high tower above 3 GeV in pT and its surounding 8 neighbors. The total cluster pT must exceed 4.5 GeV.

pT=9-11 GeV

Below is the pT of direct and decay photons from the Pythia record. Note how the two subsets are well separated at a given partonic pT. Any contamination to the direct photon signal would have to come from higher partonic pT.

gamma/pi0 separation efficiency and rejection at pT=9-11 GeV

gamma/pi0 separation efficiency and rejection at pT=9-11 GeV

gamma/pi0 separation efficiency and rejection at pT=9-11 GeV

gamma/pi0 separation efficiency and rejection at pT=9-11 GeV

Pibero DjawothoLast updated Wed Jul 4 13:23:41 EDT 2007

2007.07.09 How to run the gamma fitter

How to run the gamma fitter

The gamma fitter runs out of the box. The code consists of the classes StGammaFitter and StGammaFitterResult in CVS. After checking out a copy of offline/StGammaMaker, cd into the offline directory and run:

root4star StRoot/StGammaMaker/macros/RunGammaFitterDemo.C

The following plots will be generated on the ROOT canvas and dumped into PNG files.

Revised gamma/pi0 algorithm in 2006 p+p collisions at sqrt(s)=200 GeV

Description

computes the maximal sided residual of the SMD response in the u- and v-plane for gamma candidates. It is based on C++ code developed by Jason Webb from the original code by Les Bland who got the idea from CDF (?) The algorithm follows the steps below:

The SMD response, which is SMD strips with hits in MeV, in each plane (U and V) is stored in histogram hU and hV.

Fit functions fU and fV are created. The functional form of the SMD peak is a double-Gaussian with common mean and fixed widths. The widths were obtained by the SMD response of single photons from the EEMC slow simulator. As such, the only free parameters are the common mean and the total yield. The actual formula used is: [0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))

[0] = yield

[1] = mean

The mean is fixed to the strip with maximum energy and the yield is adjusted so the height of the fit matches that of the mean.

The residual for each side of the peak is calculated by subtracting the fit from the data (residual = data - fit) from 2 strips beyond the mean out to 40 strips.

The triggers caption in the PDF files shows the trigger id's satisfied by the event. A red trigger id is a L2-gamma trigger. I observe that generally the L2-gamma triggered event are a bit cleaner. Also shown is the pt and energy of the cluster.

Raw SMD response

Hi Pibero,
In general, I believe people have used smaller cone radii for isolation
cuts than for jet reconstruction (where the emphasis is on trying to
recover full jet energy). So you might try something like requiring
that no more than 10 or 20% of the candidate cluster E_T appears
in scalar sum p_T for tracks and towers within a cone radius of
0.3 surrounding the gamma candidate centroid, excluding the
considered cluster energy. The cluster may already contain energy
from other jet fragments, but that should be within the purview of
the gamma/pi0 discrimination algo to sort out. For comparison, Les
used a cone radius of 0.26 for isolation cuts in his original simulations
of gamma/pi0 discrimination with the endcap. Using much larger
cone radii may lead to accidental removal of too many valid gammas.
Steve

Transverse

Longitudinal

2008.01.23 Endcap etas

Endcap etas

Endcap etas

This analysis to look for etas at higher energy is in part motivated by this study. The interest in etas, of course, is that their decay photons are well separated at moderate energies (certainly more separated than the photons from pi0 decay). I ran Weihong's pi0 finder with tower seed threshold of 0.8 GeV and SMD seed threshold of 5 MeV (I believe his default SMD seed setting is 2 MeV). I then look in the 2-photon invariant mass region between 0.45 and 0.65 GeV (the PDG nominal mass for the eta is 0.54745 +/- 0.00019 GeV). I observe what looks like a faint eta peak. The dataset processed is the longitudinal 2 run of 2006 from the 20 runs sitting on the IUCF disk in Weihong's directory (/star/institutions/iucf/hew/2006ppLongRuns/).

Within the reconstructed mass window 0.45 to 0.65 GeV, I take a look at the decay photon shower profiles in the SMD. The samples are saved in the file etas.pdf. For the most part, these shower shapes are cleaner than the original sample. Although the statistics are not great.

2008.02.27 ESMD shape library

ESMD shape library

Shower Widths for Monte Carlo and Data

Description

Hal did a comparison of the widths of the shower shapes between Monte Carlo and data. Below is a description of what was done.

I took the nominal central value, either from the maxHit or the
nominal central value, and added the energy in the +/- 12 strips. Then I
computed the mean strip (which may have been different from the nominal
central value!!). I normalized the shape to give unit area for each smd
cluster, and added to the histograms separately for U and V and for MC and
data (= Will's events). I did NOT handle Will's events correctly, just
using whatever event was chosen randomly, rather than going through his
list sequentially. Note I ran 1000 events, and got 94 events in my shower
shape histos.
So, there are several minor problems. 1) I didn't go through Will's
events sequentially. 2) I normalized, but perhaps not to the correct 25
strips, because the mean strip and the nominal strip may have differed.
3) there may have been a cutoff on some events due to being close to one
end of the smd plane (near strip 0 or 287). My sense from looking at the
plots is that these don't matter much.
The conclusion is that the MC shape is significantly narrower than
the shape from Will's events, which is obviously narrower than the random
clusters we were using at first with no selection for the etas. Hence, we
are not wasting our time with this project.

2008.03.04 A second look at eta mesons in the STAR Endcap Calorimeter

A second look at eta mesons in the STAR Endcap Calorimeter

Introduction

from the 2006 pp longitudinal 2 runs and picked events tagged with the L2gamma trigger id (137641). I ran the StGammaMaker on the MuDst files from these runs and produced gamma trees. These gamma trees are available at

/star/institutions/iucf/pibero/2007/etaLong/

. Within the StGammaMaker framework, I developed code to seek candidate etas with emphasis on high purity. The macros and source files are:

Algorithm

Get the ranges of SMD U & V strips that span the volume of the seed tower

Find the strips with maximum energy within these ranges

Require that the maximum strips have more than 2 MeV in each SMD plane

Get the intersection of the maximum strips and ensures that it lies within 70% of the fiducial volume of the seed tower

Make sure the photon candidate responsible for the SMD clusters above enters and exits the same seed tower

Form a 11-strip cluster in each plane with +/-5 strips around the max strip and require that it contains 70% of the energy in a range +/-20 strips around the max strip

Require that the energy asymmetry between the 11-strip clusters in the U and V planes be less than 20%

Create a point using the energy of the seed tower and the position of the intersection of the max strips in the SMD U and V planes

Repeat until seed towers in the event are exhausted

Combine different points in the event to calculate the invariant mass

Diphoton pairs with invariant mass between 0.4 and 0.6 GeV are saved to a PDF file

Invariant mass

I fit the diphoton invariant mass with two Gaussians, one for the pi0 peak (p0-p2) and another one for the eta peak (p3-p5) plus a quadratic for the background (p6-p8). The Gaussian is of the form p0*exp(0.5*((x-p1)/p2)**2) and the quadratic is of the form p6*+p7*x+p8*x**2. A slightly better chi2/ndf in the fit is achieved by using Breit-Wigner functions instead of Gaussians for the signal here. I calculate the raw yield of etas from the fit as p3*sqrt(2*pi)*p5/bin_width = 85 where each bin is 0.010 GeV wide. I select candidate etas in the mass range 0.45 to 0.55 GeV and plot their photon response in the shower maximum detector here. Since we are interested in collecting photons of pT > 7 GeV, only those candidate photons with pT > 5 GeV will be used in the shower shape library. I also calculate the background under the signal region by integrating the background fit from 0.45 to 0.55 GeV and get 82 counts.

2008.04.08 Data-Driven Shower Shapes

Data-Driven Shower Shapes

Gamma Conversion before the Endcap

The plots below show the conversion process before the Endcap. I look at prompt photons heading towards the Endcap from a MC gamma-jet sample with a partonic pT of 9-11 GeV. I identify those photons that convert using the GEANT record. The top left plot shows the total number of direct photons and those that convert. I register a 16% conversion rate. This is consistent with Jason's 2006 SVT review. The top right plot shows the source of conversion, where most of the conversions emanate from the SVT support cone, also consistent with Jason's study. The bottom left plot shows the separation in the SMD between the projected location of the photon and the location of the electron/positron from conversion.

Background Rejection vs. Signal Efficiency

Background Rejection vs. Signal Efficiency (Neutral Meson pT > 8 GeV)

2008.04.12 Pythia Gamma-Jets

Pythia Gamma-Jets

Gamma-Jet Yields

During Run 6, the L2-gamma trigger (trigger id 137641) sampled 4717.10 nb-1 of integrated luminosity. By restricting the jet to the Barrel, |ηjet|<1, and the gamma to the Endcap, 1<ηgamma<2, the yield of gamma-jets is estimated as the product of the luminosity, the cross section, and the fraction of events in the phasespace above. The total cross section reported by Pythia for gamma-jet processes at different partonic pT thresholds is listed in the table below. No efficiencies are included.

pT threshold [GeV]

Total cross section [mb]

Fraction

Ngamma-jets

5

6.551E-05

0.0992

30654

6

3.075E-05

0.1161

16840

7

1.567E-05

0.1150

8500

8

8.654E-06

0.1131

4617

9

4.971E-06

0.1223

2868

10

2.953E-06

0.1151

1603

Gamma-Jets pT slope

The pT slope is exp(-0.69*pT)=2^(-pT), so the statistics are halved with each 1 GeV increase in pT.

2008.04.16 Jet Finder QA

Jet Finder QA

2008.04.20 BUR 2009

Partonic pT=7-9 GeV

Partonic pT=9-11 GeV

Combined Partonic pT

2008.04.22 Run 6 Photon Yield Per Trigger

Run 6 Photon Yield Per Trigger

Introduction

The purpose of this study is to estimate the photon yield per trigger in the Endcap Electromagnetic Calorimeter during Run 6. The trigger of interest is the L2-gamma trigger. Details of the STAR triggers during Run 6 were compiled in the 2006 p+p run (run 6) Trigger FAQ by Jamie Dunlop. The triggers relevant to this study are reproduced in the table below for convenience.

The luminosity sampled by each trigger was also caclulated here by Jamie Dunlop. The luminosity for the relevant triggers is reproduced in the table below for convenience. The figure-of-merit (FOM) is calculated as FOM=Luminosity*PB*PY for transverse runs and FOM=Luminosity*PB2*PY2 for longitudinal runs where PB is the polarization of the blue beam and PY is the polarization of the yellow beam. Naturally, in spin physics, the FOM is the better indicator of statistical precisison.

Trigger

First run

Last run

Luminosity [nb-1]

Figure-of-merit [nb-1]

117641

7093102

7096017

118.88

11.89

127641

7097009

7129065

3219.04

1099.43

137641

7135050

7156028

4717.10

687.65

Event selection

Trigger selection

For this study, only the trigger of longitudinal running 2 (137641) is used. As mentioned above, at level-0, an EEMC high tower above 3.5 GeV and its associated trigger patch above 4.5 GeV in transverse energy coupled with a minimum bias condition, which is simply a BBC coincidence to ensure a valid collision, is required for the trigger to fire. The EEMC has trigger patches of variable sizes depending on their location in pseudorapidity. (The BEMC has trigger patches of fixed sizes, 4x4 towers.) At level-2, a high tower above 3.7 GeV and a 3x3 patch above 5.2 GeV in transverse energy is required to accept the event.

Gamma candidates

In addition to selecting events that were tagged online by the L2-gamma trigger, the offline

looks for tower clusters with minimum transverse energy of 5 GeV. These clusters along with their associated TPC tracks, preshower and postshower tiles, and SMD strips form gamma candidates. Gamma trees for the 2006 trigger ID 137641 with primary vertex are located at

/star/institutions/iucf/pibero/2006/gammaTrees/

.

Track isolation

The gamma candidate is required to have no track pointing to any of its towers.

EMC isolation

The gamma candidate is required to have 85% of the total transverse energy in a cone of radius 0.3 in eta-phi space around the position of the gamma candidate. That is E

Tgamma

/E

Tcone

> 0.85 and R=√Δη

2

+Δφ

2

=0.3 is the cone radius.

Jet Reconstruction

The gamma candidate is matched to the best away-side jet with neutral fraction < 0.9 and cos(φ

gamma

-φ

jet

) < -0.8. The 2006 jet trees are produced by Murad Sarsour at PDSF in

/eliza13/starprod/jetTrees/2006/trees/

. A local mirror exists at RCF under the directory

/star/institutions/iucf/pibero/2006/jetTrees/

.

Spin Information

Jan Balewski has an excellent write-up, Offline spin DB at STAR, on how to get spin states. I obtain the spin states from the skim trees in the jet trees directory. In brief, the useful spin states are:

Blue Beam Polarization

Yellow Beam Polarization

Spin4

P

P

5

P

N

6

N

P

9

N

N

10

Event Summary

L2-gamma triggers

730128

Endcap gamma candidates

723848

Track isolation

246670

EMC isolation

225400

Away-side jet

99652

SMD max sided residual

19281

Barrel-only jet

15638

Note the number of L2-gamma triggers include only those events with a primary vertex and at least one gamma candidate (BEMC or EEMC).

Gamma-Jet Plots

Comparison of pT Slope with Pythia

Partonic Kinematics Reconstruction

Open Questions

I count ~2.4M events with trigger id 137641 using the Run 6 Browser, however my analysis only registers about ~0.78M.

2008.05.07 Number of Jets

Number of Jets

After selecting Endcap gamma candidates out of L2-gamma triggers, applying track and EMC isolation cuts, and matching the Endcap gamma candidate to an away-side jet, I record the number of jets below per event. Surprisingly, 8% of the events only have 1 jet. Those are events where the Endcap gamma candidate was not reconstructed as a neutral jet by the jet finder. The question is why.

I display both Barrel and Endcap calorimeter towers (the z-axis represents tower energy) and draw a circle of radius 0.3 around the gamma candidate and a circle of radius 0.7 around the away-side jet for 2006 pp200 run 7136022. Even though many of the gamma candidates not reconstructed by the jet finder are at the forward edge of the Endcap, it is not at all clear why those that are well within the detector are not being reconstructed.

2008.07.16 Extracting A_LL and DeltaG

Extracting A_LL and DeltaG

Determining state of beam polarization for Monte Carlo events

While Pythia does a pretty good job of simulating prompt photon production in p+p collisions, it does not include polarization for the colliding protons nor partons. A statistical method for assigning polarization states for each event based on ALL [1] is demonstrated in this section. For an average number of interactions for each unpolarized bunch crossing, Neff, the occurence of an event with a particular polarization state obeys a Poisson distribution with average yield of events per bunch crossing:

For simplicity, the polarizations of the blue and yellow beams are assumed to be P

B

=P

Y

=0.7 and N

eff

=0.01. The "+" spin state defines the case where both beams have the same helicities and the "-" spin state for the case of opposite helicities. The asymmetry A

LL

is calculated from the initial states polarized and unpolarized parton distribution functions and parton-level asymmetry:

The algorithm then consists in alternatively drawing a random value N

int

from the Poisson distributions with mean μ

+

and μ

-

until N

int

>0 at which point an interaction has occured and the event is assigned the current spin state. The functioning of the algorithm is illustrated in Figure 1a where an input A

LL

=0.2 was fixed and N

trials

=500 different asymmetries were calculated. Each trial integrated N

total

=300 events. It is then expected that the mean A

LL

~0.2 and the statistical precision~0.1:

Indeed, both the A

LL

and its error are reproduced. In addition, variations on the number of events per trial were investigated (N

total

) in Figure 1b. The extracted width of the Gaussian distribution for A

Event reconstruction

For this study, the gamma-jets Monte Carlo sample for all partonic pT were used. As an example, the prompt photon processes for the partonic pT bin 9-11 GeV and their total cross sections are listed in the table below. Each partonic pT bin was divided into 15 files each of 2000 events.

==============================================================================
I I I I
I Subprocess I Number of points I Sigma I
I I I I
I----------------------------------I----------------------------I (mb) I
I I I I
I N:o Type I Generated Tried I I
I I I I
==============================================================================
I I I I
I 0 All included subprocesses I 2000 9365 I 3.074E-06 I
I 14 f + fbar -> g + gamma I 331 1337 I 4.930E-07 I
I 18 f + fbar -> gamma + gamma I 2 8 I 1.941E-09 I
I 29 f + g -> f + gamma I 1667 8019 I 2.579E-06 I
I 114 g + g -> gamma + gamma I 0 1 I 1.191E-10 I
I 115 g + g -> g + gamma I 0 0 I 0.000E+00 I
I I I I
==============================================================================

The cross sections for the different partonic pT bins has been tabulated by Michael Betancourt and is reproduced here for convenience.

Partonic pT [GeV]

Cross Section [mb]

3-4

0.0002962

4-5

0.0000891

5-7

0.0000494

7-9

0.0000110

9-11

0.00000314

11-15

0.00000149

15-25

0.000000317

25-35

0.00000000990

35-45

0.000000000449

These events were processed through the 2006 pp200 analysis chain, albeit without any cuts on the SMD. The simulated quantities were taken from the Pythia record and the reconstructed ones from the analysis.

Install Pythia 8

A directory pythia8108/ will be created. Cd into it and follow the instructions in the README file to build Pythia 8. Set the environment variables PYTHIA8 and PYTHIA8DATA (preferably in /etc/profile.d/pythia8.sh):

MC invariant mass histograms are obtained from correctly associated MC Pi0s after reconstruction. No weighting of the different partonic pt samples is performed. This can (and will) introduce a bias

Then the same fitting procedure as for data is applied

The results are shown in the two figures below.

Mass:

Width:

Neutral Pion Paper: 2005 ALL & <z>

Neutral Pion Paper for 2005 data: Final Results.

There are two spin plots planned for the paper, one with the 2005 A_LL and one with the <z>. In addition to this the cross section will be included (analysis by Oleksandr used for publication).

Final result for A_LL:

Figure 1: Double longitudinal spin asymmetry for inclusive Pi0 production. The curves show predictions from NLO pQCD calculations based on the gluon distributions from the GRSV, GS-C and DSSV global analyses. The systematic error shown by the gray band does not include a 9.4% normalization uncertainty due to the polarization measurement.

Final Result for <z>:

Figure 2: Mean momentum fraction of Pi0s in their associated jet as a function of p_T for electromagnetically triggered events. The data points are plotted at the bin center in pion p_T and are not corrected for acceptance or trigger effects. Systematic errors, estimated from a variation of the cuts, are shown by the grey band underneath the data points. The lines are results from simulations with the PYTHIA event generator. The solid line includes detector effects simulated by GEANT, while the dotted line uses jet finding on the PYTHIA particle level. The inset shows the distribution of pT, π / pT, Jet for one of the bins, together with a comparison to PYTHIA with a full detector response simulation.

Below are links to details about the two results.

<z> Details

<z> Details

The goal of this analysis is to relate the neutral pions to the jets they are embedded in. The analysis is done using the common spin analysis trees, which provide the necessary tools to combine the jet and neutral pion analysis.

A neutral pion is associated to a parent jet if it is within the jet cone of 0.4 in eta and phi. To avoid edge effects in the detector, only neutral pions with 0.3 < eta < 0.7 are accepted.

Cut details:

combination HT1/HT2: below 5.7 GeV only HT1 is used, above that both HT1 and HT2 are accepted

The final result uses both HT1 and HT2 triggers, but a trigger separated study has also been done, as shown below. There, HT2 includes only those HT2 triggers that do not satisfy HT1 (because of prescale).

Figure 1: <z> for Pi0 in jets as a function of p_T for HT1 and HT2 triggers. Also shown is the mean jet p_T as a function of pion p_T.

Bin-by-Bin momentum ratio

Figure 2: Bin-by-bin ratio of pion to jet p_T. The <z> is taken from the mean of these distributions, the error is the error on the mean. A small fraction of all entries have higher Pi0 p_T than jet p_T. Similar behavior is also observed for Pythia MC with GEANT jets. This obviously increases the <z>. An alternative would be to reject those events. The agreement with MC becomes worse if this is done.

Here is the data - MC comparison for 3 of the above bins. For the simulation, the reconstruction of the Pi0 is not required to keep statistics reasonable, so the true Pi0 pt is used. However, the MC jet finding uses all momenta after Geant, this is why the edges are "smoother" in the MC plot than in the data plots. Since <z> is an average value, this is not expected to be affected by this, since on average the Pi0 pt is reconstructed right.

Figure 3: Data / MC for Bin 5: 6.7 to 8 GeV

Figure 4: Data / MC for Bin 6: 8 to 10 GeV

Figure 5: Data / MC for Bin 7: 10 to 12 GeV

A_LL Details

Details on the A_LL result and the systematic studies:

The result in numbers:

Bin

<p_T> [GeV] in bin

A_LL

stat. error

syst. error

1

4.17

0.01829

0.03358

0.01603

2

5.41

-0.01913

0.02310

0.01114

3

7.06

0.00915

0.03436

0.01343

4

9.22

-0.06381

0.06366

0.01862

A_LL as separated by trigger:

Figure 1: A_LL as a function of p_T for HT1 (black) and HT2 (red) triggers separately. HT1 here is taken as all triggers that satisfy the HT1 requirement, but not HT2. Since the HT2 prescale is one, there are very little statistics for HT1 at the highest p_T. The highest p_T point for HT1 is outside the range of the plot, and has a large error bar. The high p_T HT1 data is used in the combined result.

Systematics: Summary

Bin 1

Bin 2

Bin 3

Bin4

relative luminosity

0.0009

0.0009

0.0009

0.0009

non-longitudinal pol.

0.0003

0.0003

0.0003

0.0003

beam background

0.0012

0.0084

0.0040

0.0093

yield extraction

0.0144

0.0044

0.0102

0.0116

invariant mass background

0.0077

0.0061

0.0080

0.0108

total

0.01603

0.01114

0.01343

0.01862

The first two systematics are common to all spin analyses. The numbers here are taken from the jet analysis. No Pi0 non-longitudinal analysis has been performed due to lacking statistics. These systematics are irrelevant compared to the others.

The analysis specific systematics are determined from the data, and as such are limited by statistics. The real systematic limit of a Pi0 analysis with a very large data sit will be much lower.

For the yield extraction systematic the invariant mass cuts for the pion yield extraction are varied. The systematic is derived from the maximum change in asymmetry with changing cuts.

For the beam background, the systematic is derived by studying how much A_LL changes when the beam background is removed. This is a conservative estimate that covers the scenario that only half of the background is actually removed. The asymmetry of the background events is consistent with zero.

For the invariant mass background systematic, A_LL is extracted in three invariant mass bins outside the signal region. The amount of background under the invariant mass peak (includes combinatorics, low mass and others) is estimated from the invariant mass distribution as shown below. For all three bins, the background A_LL is consistent with zero, a "worst case" of value + 1 sigma is assumed as deviation from the signal A_LL.

Invariant mass distribution:

Figure 2: Invariant mass distribution for HT1 events, second p_T bin. The red lines are the MC expectations for Pi0 and Eta, the green line is low mass background, the magenta line is combinatoric background, the thick blue line is a pol2 expectation for the other background, the blue thinner line is the total enveloppe of all contributions, compared to the data. At low mass, the background is overestimated.

Other systematic studies: False Asymmetries

False asymmetries (parity-violating single spin asymmetries) were studied to exclude systematic problems with spin asignments and the like. Of course the absence of problems in the jet analysis with the same data set makes any issues very unlikely, since jet statistics allow much better verifications than Pi0s. Still, single spin asymmetries were studied, and no significant asymmetries were observed. For both triggers, both asymmetries (yellow and blue) and for all p_T bins the asymmetries are consistent with zero, in most cases within one sigma of zero. So there are no indications for systematic effects. The single spin asymmetries are shown below:

Figure 3: Single spin asymmetry epsilon_L for the blue beam.

Figure 4: Single spin asymmetry epsilon_L for the yellow beam.

Neutral strange particle transverse asymmetries (tpb)

Neutral strange particle transverse asymmetry analysis

Here is information regarding my analysis of transverse asymmetries in neutral strange particles using 2006 p + p TPC data. This follows-on from and expands upon the earlier analysis I did, which can still be found at star.bnl.gov/protected/strange/tpb/analysis/. Comments, questions, things-you'd-like-to-see-done and so forth are welcomed. I'll catalogue updates in my blog as I make them.

The links listed below are in 'analysis-order'; best to use these for navigation rather than the alphabetically listed links Drupal links below/in the sidebar.

Data used

Data used in analysis

Data used for this analysis is 2006 p+p 200 GeV data taken with transverse polarisation, trigger setup "ppProductionTrans". This spanned days 97 (7th April) to 129 (9th May) inclusive. Trigger bemc-jp0-etot-mb-l2jet (ID 127622) is used. A file catalogue query with the following conditions gives a list of runs for which data is available:

This generates a list of 549 runs. These runs are then compared against the spin PWG run QC (see http://www.star.bnl.gov/protected/spin/sowinski/runQC_2006) and are rejected if any of the following conditions are true:

I use a Maker class to create TTrees of event objects with V0 and spin information for these runs. Code for the Maker and Event classes can be found at /star/u/tpb/StRoot/StTSAEventMaker/ and /star/u/tpb/StRoot/StV0NanoDst/ respectively. Events are accepted only if they fulfill the following criteria:

The vertex distribution of events from each run are then checked by spin bits. A Kolmogorov test (using ROOT TH1::KolmogorovTest) is used to compare the vertex distributions for (4-bit) spin bits values 5, 6, 9 and 10. If any of the distributions are inconsistent, the run is rejected. Each run's mean event vertex z position is then plotted. Figure 1 shows the distribution, fitted with a Gaussian. A 3σ cut is applied and outlier runs rejected. 38 runs are rejected by these further cuts. The remaining 320 runs, spanning 33 RHIC fills and comprising 2,500,421 events, are used in the analysis.

Double spin asymmetry

Double spin asymmetry

I measure a double spin asymmetry defined as follows

Equation 1

where N-(anti)parallel indicates yields measured in one half of the detector when the beam polarisations are aligned (opposite) and P1 and P2 are the polarisations of the beams. Accounting for the relative luminosity, these yields are given by

Equation 2

Equation 2

where the arrows again indicate beam polarisations. Figures one and two show the fill-by-fill measurement of ATT, corrected by the beam polarisation, summed over all pT.

Figure 1: K0S ATT fill-by-fill

Figure 2: Λ ATT fill-by-fill

Energy loss identification

Energy loss particle identification

The Bethe-Bloch equation can be used to predict charged particle energy loss. Hans Bichsel's model adds to this and the Bichsel function predictions for particle energy loss are compared with measured values. Tracks with dE/dx sufficiently far from the predicted value are rejected. e.g. when selecting for Λ hyperons, the positive track is required to have dE/dx consistent with that of a proton, and the negative track consistent with that of a π-minus.

The quantity σ = sqrt(N) x log( measured dE/dx - model dE/dx ) / R is used to quantify the deviation of the measured dE/dx from the model value. N is the number of track hits used in dE/dx determination and R is a resolution factor. A cut of |σ| < 3 applied to both V0 daughter tracks was found to significantly reduce the background with no loss of signal. Figures one to three below show the invariant mass distriubtions of the V0 candidates accepted and rejected and table one summarises the results of the cut. Background rejection is more successful for (anti-)Λ than for K0S because most background tracks are pions; the selection of an (anti-)proton daughter rejects the majority of the background tracks.

Geometrical cuts

Geometrical cuts

Energy loss cuts are successful in eliminating a significant portion of the background, but further reduction is required to give a clear signal. In addition final yields are calculated by a bin counting method, which requires that the background around the signal peak has a straight line shape. Therefore additional cuts are placed on the V0 candidates based on the geometrical properties of the decay. There are five quantities on which I chose to cut:

Distance of closest approach (DCA) of the V0 candidate to the primary vertex: if the V0 candidate is a genuine particle, its momentum vector should track back to the interaction point. Spurious candidates will not necessarily do so, therefore an upper limit is placed on the approach distance of the V0 to the interaction point.

DCA between the daughter tracks: due to detector resolution the daughter tracks never precisely meet, but placing an upper limit of the minimum distance of approach reduces background from spurious track crossings.

DCAs of the positive and negative daughter tracks to the primary vertex: the daughter tracks are curved due to the magnetic field and a neutral strange particle will decay some distance from the interaction point. Therefore the daughter tracks should not extrapolate back to the primary vertex, but to some distance away from it. Placing a lower limit on this distance can reduce background from tracks originating from the interaction point.

V0 decay distance: neutral strange particles decay weakly, with cτ ~ cm, so the decay vertex should typically be displaced from the interaction point. A lower limit placed on the decay distance of the V0 helps eliminate backgrounds from particles originating at the interaction point.

I wrote a class to help perform tuning of these geometrical cut quantities (see /star/u/tpb/StRoot/StV0CutTuning/) by a "brute force" approach; different permutations of the above quantities were attempted, and the resulting mass spectra analysed to see which permutations gave the best balance of background reduction and signal retention. In addition, the consistency of the background to a straight-line shape was required. Due to the limits on statistics, signal retention was considered a greater priority than background reduction. The cut values I decided upon are summarised in table one. Figures one to three show the resulting mass spectra (data are from all runs). Yields are calculated from the integral of bins in the signal (red) region minus the integrals of bins in the background (green) regions. Poisson (√N) errors are used. The background regions are fitted with a straight line, skipping the intervening bins. The signal to background quoted is the ratio of the maximum bin content to the value of the background fit evaluated at that mass. Note that the spectra have the the dE/dx cut included in addition to the geometrical cuts.

Species

Max DCA V0 to PV*

Max DCA between daughters

Min DCA + daughter to PV

Min DCA − daughter to PV

Min V0 decay distance

K0S

1.0

1.2**

0.5

0.0**

2.0**

Λ

1.5

1.0

0.0**

0.0**

3.0

anti-Λ

2.0**

1.0

0.0**

0.0**

3.0

Table 1: Summary of geometical cuts. All cut values are in centimetres.

* primary vertex** default cut present in micro-DST

Figure 1: Final K0S mass spectrum with all cuts applied.

Figure 2: Final Λ mass spectrum with all cuts applied.

Figure 3: Final anti-Λ mass spectrum with all cuts applied.

Single spin asymmetry using cross formula

Single Spin asymmetry using cross formula

Equation one shows the cross-formula used to calculate the single spin asymmetry.

Equation 1

where N is a particle yield, L(eft) and R(ight) indicate the side of the polarised beam to which the particle is produced and arrows indicate the polarisation direction of the beam. Equation one cancels acceptance and beam luminosity and allows simply the raw yields to be used for the calculation. The asymmetry can be calculated twice; once for each beam, summing over the polarisation states of the other beam to leave it "unpolarised". I previously used only particles produced at forward η when calculating the blue beam asymmetry, and backward η for yellow, but I now sum over the full η range for each. Equations two and three give the numbers for up/down polarisation for blue (westward at STAR) and yellow (eastward) beams respectively in terms of the contributions from the four different beam polarisation permutations, and these permutations are related to spin bits numbers in table one.

Equation 2

Equation 3

(in e.g. N(upUp), The first arrow refers to yellow beam polarisation, the second to blue beam.)

Beam polarisation

4-bit spin bits

Yellow

Blue

Up

Up

5

Down

Up

6

Up

Down

9

Down

Down

10

Table 1

The raw asymmetry is calculated for each RHIC fill, then divided by the polarisation for that fill to give the physics asymmetry. Final polarisation numbers (released December 2007) are used. The error on the raw asymmetry is calculated by propagation of the √(N) errors calculated for each particle yield. The final asymmetry error incorporates the polarisation error (statistical and systematic errors summed in quadrature). The fill-by-fill asymmetries for each K0S and Λ for each beam are shown in figures one and two. Anti-Λ results shall be forthcoming. An average asymmetry is calculated by performing a straight line χ2 fit through the fill-by-fill values with ROOT. Table one summarises the asymmetry results. The asymmetry error is the error from the ROOT fit and is statistical only. All fits give a good χ2 per degree of freedom and are consistent with zero within errors.

Figure 1a: K0S blue beam asymmetry

Figure 1b: K0S yellow beam asymmetry

Figure 2a: Λ blue beam asymmetry

Figure 2b: Λ yellow beam asymmetry

The above are summed over the entire pT range available. I also divide the data into different transverse momentum bins and calculate the asymmetry as a function of pT. Figures three and four show the pT-dependent asymmetries. No pT dependence is discernible.

Figure 3a: K0S pT-dependent blue beam AN

Figure 3b: K0S pT-dependent yellow beam AN

Figure 4a: Λ pT-dependent blue beam AN

Figure 4b: Λ pT-dependent yellow beam AN

Single spin asymmetry utilising relative luminosity

Single spin asymmetry making use of relative luminosity

I also calculate the asymmetry via an alternative method, making use of Tai Sakuma's relative luminosity work. The left-right asymmetry is defined as

Equation 1

where NL is the particle yield to the left of the polarised beam. The decomposition of the up/down yields into contributions from the four different beam polarisation permutations is the same as given in the cross-asymmetry section (equations 2 and 3). Here, the yields must be scaled by the appropriate relative luminosity, giving the following relations:

Equation 2

Equation 3

The relative luminosities R4, R5 and R6 are the ratios of luminosity for, respectively, up-up, up-down and down-up bunches to that for down-down bunches. I record the particle yields for each polarisation permutation (i.e. spin bits) on a run-by-run basis, scale each by the appropriate relative luminosity for that run, then combine yields from all the runs in a given fill to give fill-by-fill yields. These are then used to calculate a fill-by-fill raw asymmetry, which is scaled by the beam polarisation. The figures below show the resultant fill-by-fill asymmetry for each beam and particle species, summed over all pT. The fits are again satisfactory, and give asymmetries consistent with zero within errors, as expected.

Figure 1a: Blue beam asymmetry for K0S

Figure 1b: Yellow beam asymmetry for K0S

Figure 2a: Blue beam asymmetry for Λ

Figure 2b: Yellow beam asymmetry for Λ

V0 decays

V0 decays

The appearance of the decay of an unobserved neutral strange particle into two observed charged daughter particles gives rise to the terminology 'V0' to describe the decay topology. The following neutral strange species have been analysed:

Species

Decay channel

Branching ratio

K0S

π+ + π-

0.692

Λ

p + π-

0.639

anti-Λ

anti-p + π+

0.639

Candidate V0s are formed by combining together all possible pairs of opposite charge-sign tracks in an event. The invariant mass of the V0 candidate under different decay hypotheses can then be determined from the track momenta and the daughter masses (e.g. for Λ the positive daughter is assumed to be a proton, the negative daughter a π-minus). Raw invariant mass spectra are shown below. The spectra contain three contributions: real particles of the species of interest; neutral strange particles of a different species; combinatorial background from chance positive/negative track crossings.

Figure 1: Invariant mass spectrum under K0s hypothesis

Figure 2: Invariant mass spectrum under Λ hypothesis

Figure 3: Invariant mass spectrum under anti-Λ hypothesis

Selection cuts are applied to the candidates to suppress the background whilst maintaining as much signal as possible. There are two methods for reducing background; energy-loss particle identification and geometrical cuts on the V0 candidates.

02 Feb

February 2008 posts

2008.02.13 Gamma-jet candidates: EEMC response

Data sample

Gamma-jet selection cuts are discussed here. There are 278 candidates found for runId=7136022.
Transverse momentum distribution for the gamma-jet candidates can be found here.

Vertex z distribution for di-jet and gamma-jet events

Figure 1: Vertex z distribution.

Red line presents gamma-jet candidates (scaled by x50). Black is for all di-jet events.
Same data on a log scale is here.

Figure 2: Average vertex z as a function of transverse momentum of the fist jet (with a largest EM energy fraction).
Red is for gamma-jet candidates. Black is for all di-jet events. Strong deviation from zero for gamma-jet candidates at pt < 5GeV?

EEMC response for the gamma-jet candidate

EEMC response event by event for all 278 gamma-jet candidate can be found in this pdf file.
Each page shows SMD/Tower energy distribution for a given event:

First row on each page shows SMD response
for the sector which has a maximum energy deposited in the EEMC Tower
(u-plane is on the left, v-plane is on the right).

In the left plot (u-plane energy distribution) numerical values for
pt of the first jet (with maximum EM fraction) and the second jet are given.

If m_{gamma gamma} value is negative, then the reconstruction procedure has failed
(for example, no uv-strips intersection found, or tower energy and uv-strips intersection point mismatch, etc).
EEMC response for these "bad" events can be found in this pdf file.

Note, that I'm still working on my fitting algorithm (which is not explained here),
and fit results and the invariant mass distribution will be updated.

It is also shown the ratio for each u/v plane
of the integrated single Gaussian fit (red line) to the total energy in the plane
(look for "gamma U/V " values on the right v-plane plot).

Second and third rows on each page show the energy deposition in the
tower, pre-shower1, pre-shower2, and post-shower as a function of eta:phi (etaBin:phiBin).

Last row shows the hit distribution in the SMD for all sectors
(u-plane on the left, v-plane of the right).

Playing with a different cuts

Trying to isolate the real gammas which hits the calorimeter,
I have sorted events into different subsets based on the following set of cuts:

EEMC gamma-jet cuts (energetic photon hits EEMC with pt similar or greater to that of the opposite jet)

if (invMass < 0) reject
if (jet2_pt > jet1_pt) reject
if (jet1_pt < 7) reject
if (minFraction < 0.75) reject (minFraction = gamma U/V - is a fraction of the integrated single Gaussian peak to the total energy in the uv-plane)

Figure 4: Sample gamma-jet candidate EEMC response
(all gamma-jet candidates selected according to these conditions can be found in this pdf file):

2008.02.27 Tower based clustering algorithm, and EEMC/BEMC candidates

Gamma-jet candidates before applying clustering algorithm

selecting di-jet events with the first jet dominated by EM energy, and the second one with a large fraction of hadronic energy:

R_EM1 >0.9 and R_EM2 < 0.9

selecting di-jet events with jets pointing opposite in azimuth:

cos(phi1 - phi2) < -0.8

requiring no charge tracks associated with a first jet (jet with a maximum EM fraction):

nCharge1 = 0

Figure 1: Transverse momentum

Figure 2: Pseudorapidity

Figure 3: Azimuthal angle

Tower based clustering algorithm

for each gamma-jet candidate finding a tower with a maximum energy associated with a jet1 (jet with a maximum EM fraction).

Calculating energy of the cluster by finding all adjacent towers and adding their energy together.

Implementing a cut based on cluster energy fraction, R_cluster, where

R_cluster is defined as a ratio of the cluster energy to the total energy in the calorimeter associated with a jet1. Note, that with a cut Ncharge1 =0, energy in the calorimeter is equal to the jet energy.

Figure 2:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys with an additional SMD isolation cut: gammaFraction >0.75GammaFraction is defined as ratio of the integral other SMD strips for the first peak to the total energy in the sector

Some observations:

Shapes are similar with or without gamma-jet 7GeV pt cut (compare Figures 1 and 2), what may indicate that shape is independent on energy (at least within our kinematic limits).

Data-driven and MC shapes are getting close to each other (Figure 4, upper left plot) when requiring no energy above threshold in both preshower layers and with suppressed contribution from pi0 background. The latter is achieved by using the information on reconstructed invariant mass of 2gamma candidates (compare Figure 3 and 4).

One interpretation of this can be that in Monte Carlo simulations the contribution from the material in front of the detector is underestimated

Energy distribution for each strip in the SMD peak does not looks like a Gaussian (Figure 5), what makes very difficult to interpret results obtained from chi2 analysis (Figure 6-8).

Figure 4: Shapes for the candidates when "simple" pi0 finder failed to find a second peak (black points shows u-plane shape only, v-plane results can be found here)

Figure 5: Strip by strip SMD energy distribution. Only 12 strips from the right side of the maximum are shown. Zero strip (first upper left plot) corresponds to the high strip in the shape Note, that already at the 3rd strip from a peak, RMS values are comparable to those for a mean, and for a higher strips numbers RMS starts to be bigger that mean. (results for u-plane only, v-plane results can be found here)

2008.04.03 chi2-shape subtraction for different Preshower conditions

Request from Hal Spinka:

Hi Ilya,

I think you gave up on the chi-squared method too quickly, and am sorry I missed the phone meeting last week. So, I would like to make a request that will hopefully take a minimal amount of your time to show that all is okay. Then, if there is a delay in getting the sided residual information out and into the beam use request, you can still fall back on the chi-squared method.

In your March 28 posting, Figure 8 at the bottom, I would like to get numerical values for the events per bin for the black curves. I won't use the preshower1>0 and preshower2=0 data, so those you don't need to send. Also, I won't use the red or blue curve information.

I think your problem has been that you normalized your curves at chi-squared/ndf = 1.4 instead of the peak. What I plan to do is to normalize the (pre1=0, pre2=0) to the (pre1=0, pre2>0) data in the peak and subtract. The (pre1=0, pre2=0) set should have some single photons, but also some multiple photons. The (pre1=0, pre2>0) should also have single photons, and more multiple photons, since the chance that one of them will convert is larger. The difference should look roughly like your blue curve, though perhaps not exactly if Pibero's mean shower shape is not perfect (which it isn't). I will do the same thing with taking the difference between (pre1>0, pre2>0) and (pre1=0, pre2=0), and again the difference should look roughly like your blue curve. The (pre1>0, pre2>0) data should have even larger fraction of multiple photons than either of the other two data sets. I would expect the two difference curves to look approximately the same.

Hope this is possible for you to do. Since our reduced chi-squared curve looks so much like the one from CDF, I am pretty confident that we are okay, but this should be checked to convince people that we are not doing anything terribly wrong.

Analysis: Simulated MuDst files were first processed through jet finder algorithm (thanks to Renee Fatemi), and later analyzed by applying gamma-jet isolation cuts (see this link for details) and studying EEMC SMD response (see below). To test the algorithm, Geant records were not used in this analysis. Further studies based on Geant records (yield estimates, etc) are ongoing.

Correlation between gamma and jet pt, eta, phi

Results from maximum sided residua study

Definitions for F_peak, D_peak, D_tail^max (D_tail^min) can be found here

Figure 5:F_peak vs maximum residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 (within a 3x3 clusters around tower with a maximum energy). Shower shape used to fit data is fixed to the shape from the previous gamma-jet study of real events (see black point on Fig.1 [upper left plot] at this page)

Postshower to SMD[uv] energy ratio

Figure 8:Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8a:Same as figure 8, but for gamma-jet candidates from the real data (no pt cuts). Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8b:Comparison between gamma-jet candidates from data with different preshower conditions. Points are normalized in peak to the case of pre1 > 0, pre2 > 0 Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8c:Comparison between gamma-jet candidates from Monte-Carlo simulations with different preshower conditions. Points are normalized in peak to the case of pre1 > 0, pre2 > 0 Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Gamma pt distribution, yield and signal to background ratio plots for a cut of R_cluster >0.99 are shown below in Figs. 1-3. One can see that by going from R_cluster>0.9 to R_cluster>0.99 improves signal to background ratio from ~ 1:10 to ~ 1:5 for gamma pt>10 GeV

Figure 2:Integrated gamma yield vs pt for R_cluster >0.99For each pt bin yield is defined as the integral from this pt up to the maximum available pt. MC results scaled to the same luminosity as data.

Figure 3:Signal to background ratio for R_cluster >0.99 (all results divided by the data) Compare this figure with that for R_cluster>0.9 (Fig. 3 at this link)

Gamma and the away side jet pt matching

Figure 4: pt asymmetry between gamma and the away side jet (R_cluster >0.9) for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background). pt cut of 7 GeV for both gamma and jet has been applied.

Figure 5: signal to background ratio (R_cluster >0.9) as a function of pt asymmetry between gamma and the away side jet pt cut of 7 GeV for both gamma and jet has been applied.

Figure 6: pt asymmetry between gamma and the away side jet (R_cluster >0.99) for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background). pt cut of 7 GeV for both gamma and jet has been applied.

Figure 7: signal to background ratio as a functio of pt asymmetry between gamma and the away side jet (R_cluster >0.99) pt cut of 7 GeV for both gamma and jet has been applied.

Figure 8: pt asymmetry between gamma and the away side jet (R_cluster >0.99) for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background). pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

Figure 9: signal to background ratio as a function of pt asymmetry between gamma and the away side jet (R_cluster >0.99) pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

Figure 1:Vertex z distribution for pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger] Note: In the upper right plot (pre1=0, pre2>0) one can see a hole in the acceptance in the range bweeeen z_vertex -10 to 30 cm (probably due to SVT construction)

Figure 1b:Vertex z distribution for pp2006 (same as Fig. 1, but on a linear scale)

Figure 2:Vertex z distribution for three different data samples MC results scaled to the same luminosity as data

Figure 3:Vertex z distribution for three different data samples pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

pi0 trees from this RCF directory has been used to regenerate etas NTuple: /star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/out_23/

Some observations:

eta-meson purity within the invariant mass region [0.5, 0.65] is about 72%

Most of the eta-candidates has detector pseudorapidity less or about 1.4, what may limits applicability of data-driven shower shapes derived from these candidates for higher pseudo-rapidity region, where we have most of the background for the gamma-jet analysis due to lack of TPC tracking

z-vertex distribution is very asymmetric, and peaked around -50cm. Only a few candidates has a positive z-vertex values.

Shapes for gammas from eta-meson decayare in a good agreement with those from MC gamma-jet sample (compare red squares with blue triangle in Fig. 2 and 3).

MC gamma-jet shapes obtained by running a full gamma-jet reconstruction algorithm, and this agreement indicates that we are able to reconstruct gamma shapes which we put in with data-driven shower shape library.

MC gamma-jet shapes match pp2006 data shapes for pre1=0 condition, where we expect to be very efficient in background rejection (compare red squares with black circles in upper plots of Fig. 2 and 3).

This indicates that we are able to reproduce EEMC SMD of direct photons with data-driven Monte-Carlo.

There is no match between Monte-Carlo QCD background jets and pp2006 data for the case when both pre-shower layer fired (pre1>0 and pre2>0). (compare green triangles with black circes in bottom right plots of Fig.2 and 3). This is the region where we know background dominates our gamma-jet candidates.

This shows that we still do not reproduce SMD response for our background events in our data-driven Monte-Carlo simulations (note, that in Monte-Carlo we replace SMD response with real shapes for all background photons the same way we do it for direct gammas).

Figure 2: Shower shapes comparison between different data sets. Shapes for gamma-jet candidates obtained with the same gamma-jet reconstruction algorithm for three different data samples (pp2006, gamma-jet and QCD jets MC). pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 3:Same as Fig. 2, but with no cuts on gamma and jet pt. All shapes are similar to those in Fig. 2 with an additional pt cuts. Note, that blue triangles are the same as in Fig. 2.

2008.05.30 Eta, phi, and pt distributions for gamma and jet from MC and pp2006 data

Figure 1: Gamma pt distribution for R_cluster >0.9. No energy in both pre-shower layer (left plot), and No energy in pre-shower1 and non-zero energy in pre-shower2 (right plot) Same figure for R_cluster>0.99 can be found here

My simple gamma-gamma finder is trying to find a second peaks (clusters) in each SMD u and v planes, match u and v plane high strip intersections, and calculate the invaraint mass from associated tower energies (3x3 cluster) according to the energy sharing between SMD clusters.

Maximum side residual plots

Definitions for side residual plot (F_peak, F_tal, D_tail) can be found here For a moment same 3-gaussian shape is used to fit SMD response for all pre-shower bins.Algo needs to be updated with a new shapes sorted by pre-shower bins.

Fit parameters sorted by various pre-shower conditions and u and v-planes can be found here There are only two free parameters in a final fit: overall amplitude [0] and mean value [1] Fit range is +-2 strips from the high strip (5 strips total).

Integrate energy from a fit within +-2 strips from high strip. This is our peak energy from fit, F_peak.

Calculate tail energies on left and right sides from the peak for both data, D_tail, and fit, F_tail. Tails are integrated up to 30 strips excluding 5 highest strips. Determine maximum difference between D_tail and F_tail:max(D_tail-F_tail). This is our maximum sided residual.

Plot F_peak vs. max(D_tail-F_tail). This is sided residual plot.

(implementation for this item is in progress) Based on MC gamma-jet sided residual plot find a line (some polynomial function) which will serve as a cut to separate signal and background. Use that cut line to calculate signal to background ratio and apply it for the real data analysis.

Figure 2: max(D_tail-F_tail) distribution (projection on horizontal axis from Fig.1) Some observations: Results for pp2006 and MC gamma-jet are consistent for pre1=0 pre2=0 case (upper left plot) Results for pp2006 and MC QCD background jets are also in agrees for pre1>0 case (lower left and right plots)

Maximum sided residual: MC vs. data comparison

Figure 2: D_peak (projection on vertical axis for Fig. 1)
Upper left plot (no pre-shower fired case) reveals some difference
between MC gamma-jet and pp2006 data at lower D_peak values.
This difference could be due to background contribution at low energies.
Still needs more statistics for MC QCD jet sample to confirm that statement.

Figure 3: max(D_tail-F_tail) (projection on horisontal axis for Fig. 1)
One can get an idea of signal/background separation (red vs. black) depending on pre-shower condition.

Figure 4: Mean < max(D_tail-F_tail) > vs. D_peak (profile on vertical axis from Fig. 1)
For gamma-jet sample average sided residual is independent on D_peak energy
and has a slight positive shift for all pre-shower>0 conditions.
For large D_peak values (D_peak>0.16) MC gamma-jet and pp2006 data results are getting close to each other.
This corresponds to higher energy gammas, where we have a better signal/background ratio,
and thus more real gammas among gamma-jet candidates from pp2006 data.
(Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3)

Figure 5: Mean < D_peak > vs. max(D_tail-F_tail) (profile on horisontal axis from Fig. 1)
For "no-preshower fired" case MC gamma-jet sample has a large average values than that from pp2006 data.
This reflects the same difference between pp2006 and MC gamma-jet sample at small D_peak values (see Fig. 2, upper left plot).
(Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3)

Figure 6: D_peak vs. gamma pt

Figure 7: D_peak vs. gamma 3x3 tower cluster energy

Figure 8: 3x3 cluster tower energy distribution

Figure 9: Gamma pt distribution

Signal/background separation

The simplest way to get signal/background separation is to draw a straight line
on sided residual plot (Fig. 1) in such a way that
it will contains most of the counts (signal) on the left side,
and use a distance to that line for both MC and pp2006 data samples
as a discriminant for signal/background separation.
To get the distance to the straight line one can rotate sided residual plot
by the angle which corresponds to the slope of this line,
and then project it on "rotated" max(D_tail-F_tail) axis.

Figure 10: Shows "rotated" sided residual plot by "5/6*(pi/2)" angle (this angle has been picked by eye).
One can see that now most of the counts for gamma-jet sample (middle column)
are on the left side from vertical axis.

Figure 11: "Rotated" max(D_tail-F_tail) [projection on horizontal axis for Fig. 10]
Cut on "Rotated" max(D_tail-F_tail) can be used for signal/background separation.
From figure below one can see much better signal/background separation than in Fig. 3

Figure 12: "Rotated" D_peak [projection on vertical axis for Fig. 10]

Optimizing the shape of s/bg separation line

Ideally, instead of straight line one needs to use
an actual shape of side residual distribution for MC gamma-jet sample.
This shape can be extracted and parametrized by the following procedure:

Get slices from sided residual plot for different D_peak values

From each slice get max(D_tail-F_tail) value
for which most of the counts appears on its left side (for example 80%),

Fit these set of points {D_peak slice, max(D_tail-F_tail)} with a polynomial function

The distance to that polynomial function can be used to determine our signal/background rejection efficiency.

This work is in progress...
Just last one figure showing shapes for 6 slices from sided plot.

Figure 13: max(D_tail-F_tail) for different slices in D_peak (scaled by the integral for each slice)

Data peak energy: D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).

Data tails: D_tail^left and D_tail^right. Energy sum from 3rd strip up to 30 strips on the left and right sides from maximum strip (excludes strips which contributes to D_peak)

Fit tails: F_tail^left and F_tail^right. Same definition as for D_tail, but integrals are calculated from a fit function.

Maximum sided residual: max(D_tail-F_tail) Maximum of the data minus fit energy on the left and right sides from the peak.

Determining cut line based on sided residual plot

Figure 1: Sided residual plot: D_peak vs. max(D_tail-F_tail) Red lines show 4th order polynomial functions, a*x^4, which have 80% of MC gamma-jet counts on the left side. These lines are obtained independently for each of pre-shower condition based on fit procedure shown in Fig. 3 below.

Figure 3: max(D_tail-F_tail) [at 80%] vs. D_peak. For each slice (bin) in D_peak variable, the max(D_tail-F_tail) value which has 80% of gamma-jet candidates on the left side are plotted.

Lines represent fits to MC gamma-jet points (shown in red) using different fit functions (linear, 2nd, 4th order polynomials: see legend for color coding). Note, that in this plot D_peak values are shown on horizontal axis. Consequently, to get 2nd order polynomial fit on sided residual plot (Fig. 1), one needs to use sqrt(D_peak) function. The same apply to 4th order polynomial function.

Figure 4: D_peak vs. horisontal distance from 4th order polinomial function to max(D_tail-F_tail) values. (compare with Fig. 1: Now 80% of MC gamma-jet counts are on the left side from vertical axis)

Figure 5: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail) [Projection on horizontal axis from Fig. 4] Based on this plot one can obtain purity, efficiency, and rejection plots (see Fig. 6 below)

Gamma-jet purity, efficiency, and QCD background rejection

Horizontal distance plotted in Fig. 5 can be used as a cut separating gamma-jet signal and QCD-jets background, and for each value of this distance one can define gamma-jet purity, efficiency, and QCD-background rejection:

gamma-jet purity is defined as the ratio of the integral on the left for MC gamma-jet data sample, N[g-jet]_left, to the sum of the integrals on the left for MC gamma-jet and QCD jets, N[QCD]_left, data samples:Purity[gamma-jet] = N[g-jet]_left/(N[g-jet]_left+N[QCD]_left)

gamma-jet efficiency is defined as the ratio of the integral on the left side for MC gamma-jet data sample, N[g-jet]_left, to the total integral for MC gamma-jet data sample, N[g-jet]:Efficiency[gamma-jet] = N[g-jet]_left/N[g-jet]

QCD background rejection is defined as the ratio of the integral on the right side for MC QCD jets data sample, N[QCD]_right, to the total integral for MC QCD jets data sample, N[QCD]:Rejection[QCD] = N[QCD]_right/N[QCD]

Both Fig. 1a vs. Fig. 1b shows good statistics for old and new (gamma-filtered) MC gamma-jet samples

Fig. 1c shows poor statistics for QCD background sample within partonic pt range 5-10GeV (only 3 counts for "pre1=0 & pre2=0" condition). Fig. 1d (new QCD sample) has much more counts in the same region, but it is still only 20-25 entries for the case when none of EEMC pre-shower layers fired (upper left corner - our purest gamma-jet sample). This may be still insufficient for a various cuts systematic study.

Fig. 2 and Fig. 3 shows nice agreement between data and MC for both old and new (gamma-filtered) MC samples. For pre-shower1>0 case this agreement persists across full range of gamma's pt (7GeV and above). Upper plots in Fig. 3 shows some difference between data and Monte-Carlo, what could be effect from l2gamma trigger, which has not been yet applied for MC events.

Fig. 1-3, upper left plots (pre1=0 pre2=0) show that
average energy per strip in data-driven gamma-jet MC (i.e. solid red square in Fig. 3)
is systematically higher than that for pp2006 data (black circles in Fig. 3).

Note, that there is an agreement between SMD shower shapes
for pp2006 data and data-driven gamma-jet simulations
if one scales them to the same peak value
(Compare red vs. black in upper left plot from Fig. 1 at this link)

Fig. 4, upper left plot (pre1=0 pre2=0):
Integrated SMD energy from 25 strips
in raw gamma-jet simulations (red line) match pp2006 data (black line)
in the region where signal to background ratio is high, E_smd(25-strips)>0.1GeV.
This indicates that raw MC does a good job in
reproducing total energy deposited by direct photon.

Fig. 5, upper left plot (pre1=0 pre2=0):
There is mismatch between distributions of energy in 25 strips cluster
from data-driven gamma-jet simulations and pp2006 data.
This probably reflects the way we scale our library shower shapes
in data-driven shower shape replacement procedure.
Currently, the scaling factor for the library shape is calculated based on the ratio
of direct photon energy from Geant record to the energy of the library photon.
Our library is build out of photons from eta-meson decay,
which has been reconstructed by running pi0 finder.
The purity of the library is about 70% (see Fig. 1 at this post for more details).

The improvement of scaling procedure could be to
preserve total SMD energy deposited within 25 strips from raw MC,
and use that energy to scale shower shapes from the library.

Figure 5:25 strips SMD cluster energy for raw Monte-Carlo Note, the difference between results in Fig. 4 and 5. for MC gamma-jets (shown in red) at low energy (Esmd < 0.04) for pre1=0 pre2=0 case. This effect is due to the "Number of strips fired in 5-strips cluster > 3" cut. In data-driven Monte-Carlo we may have shower shapes with small number of strips fired (rejected in raw Monte-Carlo) to be replaced by library shape with different (bigger) number of strips fired. This mostly affects photons which starts to shower later in the detector and only fires few strips (pre1=0 pre2=0 case)

Some observation

For pre1>0 condition (contains most of events) yield in Monte-Carlo for eta > 1.5 case is about factor of two different than that from pp2006 data, while for eta < 1.5 Monte-Carlo yield agrees with data within 10-15%. This could be due to trigger effect?

For pre1=0 case yiled for both eta > 1.5 and eta < 1.5 are different in data and MC This could be due to migration of counts from pre1=0 to pre1>0 in pp2006 data due to more material budget than it is Monte-Carlo

For pre1=0 condition pp2006 data shapes are not reproduced by gamma-jet Monte-Carlo. With a larger cluster size (2x1 -> 3x3) the pp2006 and MC gamma-jet shapes are getting closer to each other.

Fig.3 [lower left, 5th bin] shows that in the region were we do not have TPC tracking (photon eta > 1.5) charge particle veto is not efficient, although there is still some improvement from this cut. This probably due to tracks with eta <1.5 which fall into large isolation radius r=0.7.

Yield vs. various analysis cuts

List of cuts (sorted according to bin number in Figs. 1-3. [No SMD sided residual cuts]):

N_events : total number of di-jet events found by the jet-finder

cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi

R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy R_{3x3cluster}>0.9 for Fig. 1, and it is disabled in Fig. 2 and 3

R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet

N_ch=0 : no charge tracks associated with a gamma candidate

N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)

Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster

Figure 1: Number of accepted events vs. various analysis cuts The starting number of events (shown in first bin of the plots) is the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV upper left: cuts applied independently upper right: expept this cut fired (event passed all other cuts and being rejected by this cut)lower left: "cuts applied independently" normalized by the total number of eventslower right: "expept this cut fired" normalized by the total number of events

Figure 2: Same as Fig.1 except: no R_cluster cut and photon detector eta < 1.5 (eta region where we do have most of the TPC tracking)

Cuts applied

Data driven shower shape replacement maker fix

Figure 1: (reproducing old results with dd-maker fix)transverse momentum and vertex z distributions
before (with ideal gains/pedestals) and
after (with realistic gains/pedestal tables) dd-maker fix are in a good agreement.
For details on "dd-maker problem", read these hyper news threads:emc2:2905, emc2:2900, and phana:294
Now we can run L2gamma trigger emulation and Eemc SMD ddMaker
in the same analysis chain.

l2-gamma trigger effect in simulation

Figure 2: Vertex z distribution with and without trigger condition in simulations
(emulated trigger: eemc-http-mb-L2gamma [id:137641]).
Solid red/green symbols show results with l2gamma condition applied,
while red/green lines show results for the same analysis cuts but without trigger condition.
Note, good agreement between MC QCD jets with trigger condition on (green solid squared)
and pp2006 data (black solid circles) for pre-shower1>0 case.

Figures

All figures:

All pre-shower conditions combined, No pre-shower cuts

Thick blue line shows MC sum: QCD + gamma-jet

Black solid circles: pp2006 data

Monte-Carlo results first scaled to 3.164 pb^-1 according to Pythia luminosity and then an additional fudge factor of 1.24 has been applied. Fudge factor is defined as the yields ratio from data to scaled with Pythia luminosity Monte-Carlo for pt_jet>7GeV and pt_gamma>7 candidates

Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates

Figures

Green line shows MC sum: QCD + gamma-jet Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates

Observations

Pre-shower energy distributions from pp2008 data set are narrower than that for pp2006 data. This corresponds to smaller amount of material budget in y2008 STAR geometry.

Pre-shower energy distribution from Monte-Carlo with y2006 geometry closer follows the distribution from pp2008 data set, rather than that from pp2006 data. This indicates the lack of material budget in y2006 Monte-Carlo.

Note: There is a "pre-shower sector 10 problem" for pp2008 data, which results in migration of small fraction of events with pre-shower>0 into pre-shower=0 bin (first zero bins in Fig.1 and 2. below). For pre-shower>0 case this only affects overall normalization of pp2008 data, but not the shape of pre-shower energy distributions. I'm running jet-finder+my software to get more statistics from pp2008 data set, and after more QA will produce list of runs with "pre-shower sector 10 problem", so to exclude them in the next iteration of my plots.

Figures

Green line shows MC sum: QCD + gamma-jet Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates

Kinematics

Figure 1: vertex z

Figure 2: photon detector eta

Figure 3: jet detector eta

Figure 4: photon pt

Figure 5: jet pt

Figure 6: gamma-jet pt balance

Figure 7: Photon neutral energy fraction

Figure 8: Jet neutral energy fraction

Figure 9: cos(phi_gamma-phi_jet)

Photon candidate's 2x1, 2x2, and 3x3 tower cluser energy

Figure 10: 3x3 cluster energy

Figure 11: 2x1 cluster energy

Figure 12: 2x2 cluster energy

Number of charge tracks, Barrel and Endcap towers within r=0.7 for photon and gamma

Figures

Each figure has:

pp2008 data scaled to match the integraled yield from pp2006 data

mc2006 stand for MC sum: QCD + gamma-jet Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates

03 Mar

March 2009 posts

2009.03.02 Application of the neural network for the cut optimization (zero try)

Multilayer perceptron (feedforward neural networks)

Multilayer perceptron (MLP) is feedforward neural networks trained with the standard backpropagation algorithm. They are supervised networks so they require a desired response to be trained. They learn how to transform input data into a desired response, so they are widely used for pattern classification. With one or two hidden layers, they can approximate virtually any input-output map. They have been shown to approximate the performance of optimal statistical classifiers in difficult problems.

Figure 3: Data to Monte-Carlo comparison for LDA (upper plots) and MLP (lower plots)
Good (within ~ 10%) match between data nad Monte-Carlo
a) up to 0.8 for LDA discriminant, and b) up to -0.7 for MLP.

The number of strips in SMD u or v planes is required to be greater than 3

Figure 1: SMD energy in 25 central strips sorted by pre-shower energy

Upper left: pre1=0, pre2=0

Upper right: pre1=0, pre2>0

Lower left: 0<4MeV

Lower right: 4<10MeV

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samplesColour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD (combined plot for all pre-shoer bins can be found here)

2009.05.06 Applying cuts on LDA: request minimum purity or efficiency

Cut optimization with Fisher's LDA classifier

LDA for various pre-shower bins is trained independetly, and later results with pre-shower1<0.01 are combined.

There are a set of plots for various photon pt cuts (pt> 7, 8, 9 10 GeV) and with different selection of cutoff for LDA (either based on purity or efficiency). Number in brackets shows the total yield for the sample.

Fig. 2 shows that on average in GEANT Monte-Carlo we miss ~1GeV independent on the photon pt.
EEMC detector response can be still linear even if the ratio in Fig. 1 is not flat.

Usage of fixed 1.3 (or others, like 1.25) fudge factors are not justified.

It seems that using pt-dependent fudge factor (like it is done in this Jason's study)
is also unjustified, since the same effects (flat ratio of pt_reco/pt_true ~ 1)
can be reached by subtracting 1 GeV from the cluster energy (See Fig. 3).

Before further pursuing our efforts in tuning the tower energy response in the Monte-Carlo,
needs to address the observed energy loss difference in the fisrt layer of the BEMC/EEMC detector.
See Jason's blog post from 2009.07.16 for more details: Comparison muon energy deposit in the 1st BEMC/EEMC layers

Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
what assumes fixed sampling fraction of 0.05 (5%)

Vertex z=0

~50K/per particle type

Non-zero energy: 3 sigma above pedestal

Color coding:

Black - photon (single particle/event MC)

Red - electron (single particle/event MC)

Green - neutral pion (single particle/event MC)

Blue - photons from eta-meson decay (real data)

Single particle shower shape before (left) and after (right) EEMC cAir bug fixed
Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip).

Figure 1: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 2: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 3: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 4: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

Shower shape ratios

Results only for corrected EEMC geometry
All shapes are divided by MC single-photon shower shape.

Using A2Emaker to get reconstructed Tower/SMD energy
(with/without EEMC SlowSimulator in chain)

Vertex z=0

~50K/per particle type

Non-zero energy: 3 sigma above pedestal

Color coding:

Photon (single particle MC)

Electron (single particle MC)

Neutral pion (single particle MC)

Eta-meson (single particle MC)

Eta-meson [pp2006 data] (single photons from eta-meson decay)

Pre-shower bins:

Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)

Ep1 = 0, Ep2 > 0

0 < Ep1 < 4 MeV

4 < Ep1 < 10 MeV

Ep1 > 10 MeV

All pre-shower bins combined

Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single particle MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson from pp2006 data the sum is over 3x3 tower patch

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Note: Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single photon MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson/gamma-jet candidates from pp2006 data the sum is over 3x3 tower patch

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Sampling fraction

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 2: Shower shapes

Shower shapes sorted by pre-shower energy

Pre-shower bins:

Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)

Ep1 = 0, Ep2 > 0

0 < Ep1 < 4 MeV

4 < Ep1 < 10 MeV

Ep1 > 10 MeV

All pre-shower bins combined

Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single particle MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson from pp2006 data the sum is over 3x3 tower patch

Figure 3: Shower shapes (left) and their ratio (right)

Figure 4: Shower shape ratios

2009.10.05 Fix to the Jason geometry file

Why volume numbers has changed in Jason geometry file?

The number of nested volumes (nv),
is the total number of parent volumes for the sensitive volume
(sensitive volume is indicated by the HITS in the tree structure below).

For the Jason and CVS files this nv number seems to be the same
(see block tree structures below).
Then why volume ids id in g2t tables has changed?

The answer I found (which seems trivial to me know)
is that in the original (CVS) file ECAL
block has been instantiated (positioned) twice.
The second appearance is the prototype (East) version of the Endcap
(Original ecalgeo.g from CVS)

if (emcg_OnOff==1| emcg_OnOff==3) then Position ECAL in CAVE z=+centerendifif (emcg_OnOff==2| emcg_OnOff==3) then Position ECAL in CAVE z=-center ThetaZ=180endif

In Jason version the second appearance has been removed
(what seems natural and it should not have any effect)
(ecalgeo.g Jason edits, g23):

IF (emcg_OnOff>0) THEN Create ECAL
.....
IF (emcg_OnOff==2 ) THEN Prin1 ('East Endcap has been removed from the geometry' )ENDIF EndIF! emcg_OnOff